The present invention relates to a method, a memory medium and an apparatus implementing and performing a contrast-based dynamic range management (xe2x80x9cC-DRMxe2x80x9d) algorithm, for compressing an intensity dynamic range of an input image to a reduced intensity dynamic range supported by an available display device and, moreover, for doing so in a fashion which maximizes the displayed image contrast and detail. The compression is performed, in accordance with the invention, by directly and separately managing the mean (low frequency) and contrast (high frequency) content of the input image. This affords more deterministic behavior and reduced complexity, while enabling automatic adaptation to the dynamic range of the image, and results in minimizing the extent of compression required to enable displaying the image with optimized contrast on an available display device. The invention more particularly relates to such a method, a memory medium and an apparatus applicable to cardiac x-ray imaging and accordingly operable at video rates (typically 30 frames per second) and having low latency (i.e., a time duration from image acquisition to display of around 150 ms and thus effectively producing a real time display) to enable eye-hand coordination and which does not require interactive tuning of performance by a physician, permitting the physician to concentrate on performing an on-going medical procedure in reliance upon the displayed images, e.g., guiding a catheter through a blood vessel of a patient, without being distracted by any requirement for interactive tuning of the image acquisition, processing and display apparatus.
Digital X-ray imaging is a well-known, non-contact technique for observing, in real time, interior aspects of an object. In practice, an X-ray beam is generated and targeted on the object of interest. A detecting device is positioned on the other side of the object and detects the X-rays transmitted through the object. The detected X-ray signals are converted to digital signals that represent various features in the object, are further processed, and the resulting signal is displayed on a display device such as a CRT.
One of the fundamental image processing problems in digital X-ray imaging is the need to transform the intensity dynamic range of the input image to the dynamic range supported by an available display device. Typically, the intensity dynamic range of an image exceeds the dynamic range of the display mechanism by several times. The objective of the transformation accordingly is to compress the xe2x80x9cDCxe2x80x9d or mean component of the different regions comprising the image so that the dynamic range (typically 256 gray levels) of an available display device may be utilized in a fashion which maximizes the displayed image contrastxe2x80x94taking into account, as well, the generally recognized limitation of the human eye of discerning only 256 gray levels.
Common approaches to achieving such transformations are known as xe2x80x9cunsharp maskingxe2x80x9d and xe2x80x9cextended dynamic range (EDR)xe2x80x9d, the latter a special adaptation of the former. The conventional approach to performing the EDR algorithm, in general, is simply to subtract a portion of the mean from the input signal. However, in some situations, this approach results in important contrast (higher frequency) information either being removed from the image or being artificially enhanced and thereby introducing artifacts. In certain improved implementations of the EDR algorithm, however, compression is less likely to result in a loss of higher frequency contrast information or an introduction of artifacts.
A more recent type of EDR function, employed in a cardiac feasibility study relating to a predecessor system relative to the present invention, is computed and implemented in the following manner:
y(i,j)=GAMMA[x(i,j)xe2x88x92BOOST[{overscore (x)}(i,j)]],xe2x80x83xe2x80x83(1)
where:
y(i,j) is the (i,j)th pixel value of the output image;
x(i,j) is the (i,j)th pixel value of the input image; and
{overscore (x)}(i, j), i.e., (x_bar(i,j)), is the local spatial mean intensity value of the (i,j)th pixel, derived from a BOXCAR average. (In practice, intensity is directly related to X-ray count, but the relationship is rather complex. The x-rays are converted to photons which, in turn, are converted the electrons, in an x-ray imager. The Electrons are then digitized by an analog-to-digital converter (xe2x80x9cA/Dxe2x80x9d or xe2x80x9cADCxe2x80x9d) and basic image corrections are performed, such as adjustments to gain, offset and scaling, after which the image is ready to be processed for display.)
A graphical representation of the EDR processing is shown in FIG. 1. The intensity value x of an input pixel (i,j) of an image is first processed by a BOXCAR (moving average) function 12 that determines the local mean intensity value at that (i,j) pixel location. (An xe2x80x9cxxe2x80x9d is used herein to designate an input intensity at a pixel location and, thus constitutes an individual scaler value; by contrast, an xe2x80x9cXxe2x80x9d designates the intensity image value, and thus is a vector value.) The BOXCAR function 12 utilizes a neighborhood of pixels, which includes and is centered on the input pixel, to calculate the local spatial mean intensity value {overscore (x)}(i,j)xe2x80x94(see, terms of Equation (1), supra).
As illustrated in FIG. 1, BOOST LUT 14 comprises a look-up table (LUT) which specifies the intensity reduction of the input image signal x(i,j) as a function of the local spatial mean intensity value {overscore (x)}(i,j). An adder (ADD) 18 combines the (negative) output of BOOST LUT 14 and the (positive) output of LUT 16 (see Equation (1)) and supplies the result to GAMMA LUT 20, which then compresses the result of the unsharp masking, or subtraction, operation of an ADD 18 to 256 levels (8 bits per pixel, or 8 bpp). The LUTs 14 and 20 are indexed by the appropriate pixel intensity values given in equation (1). Thus, each of the BOOST LUT 14 and the GAMMA LUT 20 jointly manages both mean and contrast modification functions. Thus, this more current EDR processing algorithm, while an improvement over previous compression/transformation algorithms (e.g., which merely subtracted the local mean intensity signal from the input signal), is relatively complex, yet does not permit simultaneous, independent control of the mean and contrast modification functions.
Another problem with the current EDR processing algorithm is that of inconsistent contrast management resulting in exaggeration of negative contrast regions in the image. When a region in the image, such as a vessel filled with dye, for example, has an image intensity which is less than the surrounding local mean intensity value, EDR processing may exaggerate the negative contrast associated with the darker region when it subtracts the local mean intensity values from the intensity values associated with the darker region. This exaggerated negative contrast may result in artifacts, which can lead to misdiagnosis.
Where an image includes multiple areas having potentially differing mean levels of gray, or when images of objects embedded in such areas have respective, different gray levels, or if respective gray levels of an object and its background have similar values, the contrast parameters of the display window must be adjusted to enhance the visibility of these differences in order to obtain a diagnosis of the underlying structure being imaged. Thus, when a viewer""s attention is shifted from one object to another, where the contrast of one combination of object and background differs significantly from the contrast of another combination of object and background, various display window parameters relating to contrast adjustment must be changed. Without such adjustments, the image will appear either excessively faint or excessively bright, such that all detail critical to an effective diagnosis is absent. As a result, in order to obtain a diagnosis, it is often necessary, during the course of shifting attention among areas of differing contrast, for the physician to make numerous contrast adjustments to the display window. This can be disruptive in, e.g., radiology and mammography procedures and is altogether unacceptable in cardiac procedures, underway at the time.
Contrast of a cardiac digital X-ray image must be managed in a deterministic and consistent manner to achieve optimum results. For example, cardiologists perform diagnoses by examining the apparent thickness of a coronary artery, as revealed in X-ray imaging by a contrast medium injection. Because of X-ray physics, the artery thickness may lead to a modulation of the underlying background gray-level, i.e., contrast. Contrast consistency, particularly in dye filled vessels, is important, since processing-induced contrast changes in vessels may be interpreted as coronary disease leading to misdiagnosis. Consequently, it is desirable that artery contrast (i.e., not intensity) pass through DRM processing with deterministic and linear gain. However, minimal and deterministic modification to contrast can be tolerated, particularly in X-ray imaging of the lungs, where there is significant compression of the mean and minimal clinically relevant information. In fluoroscopic mode, the cardiologist is focused on the placement of interventional devices, and the rendering of the corresponding contrast is more directed by the visibility of these tools. In prior systems, however, contrast functions were managed in a non-linear manner, which varied with the local meanxe2x80x94leading to artifacts and confusion and resulting, in at least some cases, in increased patient exposure in efforts to position the X-ray tube and an image intensifier such that a vasculature of interest, e.g., over a spine diagram, could be viewed satisfactorily.
Accordingly, it is desirable to provide a compression algorithm that overcomes the deficiencies of the aforementioned EDR processing algorithms and which permits managing the mean (low frequency) and contrast (high frequency) content of an image, separately and directly. Further, it is desirable to provide a compression algorithm that overcomes problems associated with inconsistent contrast management. Yet other limitations exist in even the more current EDR processing algorithm. For example, an X-ray imaging system is generally provided with controllable settings that allow the user to manually select one of three dynamic ranges. The EDR processor then subtracts a particular percentage of the local mean intensity value from the input image intensity value, based on the setting selected by the user. If the user fails to select the appropriate setting which best accommodates the dynamic range of a given image, the displayed image may have poor image quality. In many cases, this may result in the loss of more high frequency contrast information than is necessary to perform the compression.
It accordingly is desirable to provide a compression algorithm which adaptively adjusts to the dynamic range of the image, so that high frequency contrast information is preserved, while applying minimal compression to display the image in a more deterministic manner and with reduced complexity.
The invention provides a method, a related memory medium and an apparatus for implementing and performing a contrast-based dynamic range management (C-DRM) algorithm, to compress input image data of a broad dynamic range of intensities to a reduced dynamic range (e.g., 256 levels) of an available display device, by managing the image""s mean (low frequency) and contrast (high-frequency) content, separately and directly. A BOXCAR (moving average) device takes an intensity image, as input, and produces an output image in which the intensity of each pixel represents the local mean intensity of the input image. In a first processing path, a local mean estimate of the intensity of a pixel of the input image is processed by a mean modification function to produce a modified local mean intensity of the pixel. In a second path, independent of the first path, the intensity of the corresponding pixel is processed in relation to the local mean estimate thereof to produce a contrast estimate, the local mean estimate is processed with a contrast modification function to produce a result. The result then is combined with the contrast estimate to produce a modified contrast estimate which then is combined with the modified local mean to produce a modified output intensity for the corresponding pixel in the reduced intensity dynamic range. While a BOXCAR moving average is used in the disclosed embodiments herein, the specific method used for computing the local mean estimate is not essential to the C-DRM processing.
In a further embodiment, C-DRM is implemented so as to be adaptive and thereby to follow variations in image intensities while panning, but without causing flicker or delay modifications in the resulting display. In yet further embodiments, function modification of contrast and of mean is implemented by respective multiple look-up tables (LUTs), the specific table for each being selected, based on a maximum intensity value for the image as a whole. However, the use of multiple LUTs for adaptive range compression is not essential to the central concept herein of separately and directly managing mean and contrast components of the image data.